The Factor Structure and Factorial Invariance for the Decisional Balance Scale for Adolescent Smoking

Int. J. Behav. Med. (2009) 16:158 163 DOI 10.1007/s12529-008-9021-5 The Factor Structure and Factorial Invariance for the Decisional Balance Scale for Adolescent Smoking Boliang Guo & Paul Aveyard & Antony Fielding & Stephen Sutton Published online: 19 February 2009 # International Society of Behavioral Medicine 2008 Abstract Background The transtheoretical model is a framework to explain smoking uptake and cessation in adolescence. Decisional balance is proposed as a driver of stage movement. Purpose The purpose of this study was to examine the factor structure and measurement equivalence/ (ME/I) of the decisional balance scale. Methods In this study, we used confirmatory factor analysis followed by measurement equivalence/ testing to examine the factorial validity of the decisional balance scale in adolescent smokers and nonsmokers. Results Unlike previous studies, we found that a four-factor solution splitting cons into esthetic and health cons significantly improved the fit of model to the data. ME/I testing showed that the same structure and measurement model held for both smokers and nonsmokers, girls and boys, and across the three occasions the scale was administered. Conclusions Cons showed strong evidence that it constituted two separate first order factors. Decisional balance for smoking in adolescence has good evidence of factorial validity. Electronic supplementary material The online version of this article (doi:10.1007/s12529-008-9021-5) contains supplementary material, which is available to authorized users. B. Guo : P. Aveyard (*) Division of Primary Care & Public Health, University of Birmingham, Birmingham B15 2TT, UK e-mail: p.n.aveyard@bham.ac.uk A. Fielding Department of Economics, University of Birmingham, Birmingham B15 2TT, UK S. Sutton Department of Public Health and Primary Care, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 2SR, UK Keywords Decisional balance scale. TTM. Factor structure. Measurement test. Longitudinal study Introduction The transtheoretical model (TTM) proposes that individuals changing behavior pass through a series of stages. Movement through the stages is driven by changes in decisional balance, temptation/self-efficacy, and the processes of change [1, 2]. The model is influential in health psychology, particularly in studies using stage as an outcome or intermediate outcome variable, or being the inspiration for stage-based interventions. In this study, we examine the psychometric properties of the decisional balance scale for adolescent nonsmokers and smokers, which, aside from stage, is the main variable used in TTMbased studies. The measure of decisional balance was first derived from the work of Janis and Mann [2]. Velicer et al. [2] tested a 24-item questionnaire of the pros and cons of smoking with two five-item response formats frequency and importance. Both response formats gave nearly identical results on principal components analysis and gave a twocomponent solution. For adults, the items focus on the pharmacological effect of smoking (the pros) and health risks and embarrassment (cons). The decisional balance scale has been subsequently used with the importance response scales but also with the agreement scales for some behaviors in adults [3]. Stern et al. [4] developed the adult version into a sixitem pros and a six-items cons measure for adolescent smoking, though details of this process are described in an unpublished thesis. Pallonen et al. [5] investigated the factor structure of decisional balance for adolescents and found a single cons factor with two pros factors coping pros and social pros that were each first-order factors. They used

Int. J. Behav. Med. (2009) 16:158 163 159 the agree disagree response format. Details of the model fitting and alternative models pursued are not described. A more comprehensive investigation of the factor structure of the decisional balance scale was undertaken by Plummer et al. [6], who fitted sequentially one-, two-, and three-factor models for both smokers and nonsmokers separately and found that the three-factor solution was preferred, once again finding pros divided into social and coping pros. They used the important not important format. However, the fit of these models was only reasonable, with comparative fit indices (CFI) of 0.957 and root mean square error of approximation (RMSEA) of 0.070 for smokers and 0.963 and 0.055 for nonsmokers. Alternative models were not explored. These investigators found differences in the means of these constructs between stages in a cross-sectional sample that were somewhat consonant with expectations from the TTM, as did Pallonen et al. [5]. These results might be interpreted as weak evidence of predictive validity. Thus far, therefore, there is limited validity testing of this key construct in the TTM in adolescents. We therefore used measurement equivalence (ME/I) testing to examine evidence for the construct validity of the decisional balance scale. ME/I is a technique designed to show whether a given scale means the same thing to different groups of people [7, 8]. It is therefore a logical prerequisite for interpreting differences in scale scores between groups, for example the between stage differences reported by Plummer et al. [6]. Given the TTM hypothesis that individuals move stage by shifts in decisional balance [9], it is important to examine ME/I to interpret previous reported differences in mean scores between groups. We tested this by examining differences between smokers and nonsmokers and between genders across measurement occasions. Methods Participants and Measures Data were from a previously reported trial of smoking prevention and cessation [10, 11]. Full details of the participants smoking behavior and demographics were reported in the trial reports. The prevalence of smoking and demographic characteristics was broadly similar to all UK at that time. Year-9 pupils (aged 13 14 years) were approached to participate and 8,352 students were enrolled in the trial. Twenty-six schools were randomized to intervention and 26 schools to control (no intervention). Only pupils in the intervention schools participated in the computer program that assessed all constructs of the TTM and gave feedback on the responses. Of the 4,112 pupils in intervention schools that completed the baseline questionnaire and initial computer session, 3,194 (77.7%) used it on all three occasions and data from this group were used in the following analyses. Of the 4,112 pupils, 1,160 were current or former smokers. The decisional balance questionnaire contained the 12 questions of the adolescent decisional balance scale, using a five-point scale with the important not important response format. Pupils were unable to omit items, as the computer program did not allow them to proceed if they did so. Unless a pupil was absent, there were no missing data. The computer program was used three times in year 9, approximately 3 months apart. Confirmatory Factor Analysis The studies of Plummer et al. [6] showed the same factor structure for adolescents, in which three items loaded on social pros, three loaded on coping pros, and six items loaded on cons. We felt that the cons scale was conceptually best divided into two: smoking stinks, smoking is a messy habit, and smoking makes teeth yellow, constituting esthetic cons and smoking can affect health of others, smoking cigarettes is hazardous to people s health, and cigarette smoke bothers other people, which we termed health cons. Data were analyzed separately for each occasion the questionnaire was administered. We tested sequentially two- (pros and cons), three- as in Pallonen et al. [5] and Plummer et al. [6], and four-factor models (with cons divided) with Mplus 4.21. Several indices in addition to χ 2 were used to judge model fit [12]. For the CFI and the nonnormed fit index (NNFI) values above 0.90 generally indicate models with acceptable fit [8]. A RMSEA below 0.08 usually indicates reasonable fit [13], with a threshold of 0.05 providing a stricter criterion of goodness of fit. Standardized root mean square residual (SRMR) values below 0.08 also indicate excellent model fit [14]. Akaike Information Criterion (AIC) was used to compare nonnested models, with smaller values indicating improvement in fit [15, 16]. ME/I Testing ME/I testing was used to examine whether there were differences in meaning of the questionnaire items and or factor structure across measurement waves, between smokers and nonsmokers and between males and females. We tested this sequentially with seven nested models for ME/I testing, with more restrictions on later models [8, 17]. The first model (M1) we tested was configural. This tests whether the factor structure was the same on each occasion, meaning that the pattern of factor loadings on the indicators was the same across measurement waves, between gender groups, and between smoker/nonsmoker groups. The following set of sequential restrictions were tested: equal factor

160 Int. J. Behav. Med. (2009) 16:158 163 Table 1 Tests of alternative model fit for data from the first occasion Model df χ2 P-χ2 CFI NNFI RMSEA SRMR Δχ2 Δdf P-Δχ2 ΔCFI AIC M1: two-factor model M2: three-factor model (M2 vs M1) M3: four-factor model (M3 vs M2) 53 2,583.018 0.000 0.844 0.805 0.105 0.067 140,329.525 51 919.501 0.000 0.946 0.931 0.063 0.043 1,663.517 2 0.000 0.102 138,670.008 48 550.724 0.000 0.969 0.957 0.049 0.033 368.777 3 0.000 0.023 138,307.231 Δχ 2 χ 2 for prior model χ 2 for postmodel, Δdf df for prior model df for postmodel, ΔCFI CFI for postmodel CFI for prior model loadings for like items (metric, M2), equal intercepts of like items regression on the latent variable(s) (scalar, M3), and equality of like items residual variance (invariant uniqueness, M6). These four models test measurement. It is necessary to establish measurement to interpret differences between factor means for two or more groups of interest (e.g., health cons between smokers and nonsmokers). Structural tests examine whether there are substantive differences in the factors of interest between the two groups in question (here, measurement waves, smokers and nonsmokers, and between genders [8, 17]). The invariant factor means model (M7) tests whether the mean of each factor was similar on each occasion (for example). The invariant factor variance model (M4) tests whether the factor variance differed across measurement occasion. This test examines whether the respondents used an equal range of response options in completing the questionnaire items on each occasion. The final model was the invariant factor covariance model (M5), which tests whether the strength of association between the factors (correlations between factors) differed between measurement occasions. Model comparisons were judged by reference to the χ 2 change test. However, if the sample size is large, even trivial differences result in a significant value of χ 2 change, which means rejecting the null hypothesis that there is no real difference between models [8, 18 20]. The CFI change is independent of both model complexity and sample size and not correlated with the overall fit measurements. A reduction of 0.01 or more in CFI indicates the null hypothesis of no difference should be rejected. We therefore mainly judged model improvement on the CFI change [8, 19]. As the same individuals were measured serially, the ME/I analyses were based on augmented covariance matrix [8]. Q1 Smoking makes people get more respect from others Q3 Teenagers who smoke have more friends Q11Teenagers who smoke have more boy/girl friends.666.783.689 Social pros.576 Q5 Smoking helps people to cope better with frustrations.793 -.077 Q7 Smoking cigarettes is pleasurable Q9 Smoking cigarettes relieves tension.693.836 Coping pros -.260 -.223 Q2 Smoking stinks Q10 Smoking is a messy habit Q12 Smoking makes teeth yellow.672.693.606 Aesthetic cons.794 -.340 Q4 Smoking can affect the health of others Q6 Smoking cigarettes is hazardous to people s health.669.666 Health cons Q8 Cigarette smoke bothers other people.718 Fig. 1 Standardized loadings and correlations for the four-factor model for decisional balance on the first occasion (round 1 PC data)

Int. J. Behav. Med. (2009) 16:158 163 161 Table 2 ME/I fit indices for the questionnaire data across measurement waves Model df χ 2 P-χ 2 CFI NNFI RMSEA SRMR Δχ 2 Δdf P- Δχ 2 ΔCFI AIC M1. Configural M2. Factor loading M3: Scalar M4: Factor variance M5: Factor covariance M6: Equal uniqueness M7: Mean 492 1,931.668 0.000 0.974 0.967 0.030 0.032 292,683.450 508 2,001.079 0.000 0.973 0.967 0.030 0.034 69.411 16 0.000 0.001 292,720.861 524 2,175.100 0.000 0.970 0.964 0.031 0.033 174.021 16 0.000 0.003 292,934.881 532 2,526.476 0.000 0.964 0.957 0.034 0.059 351.376 8 0.000 0.006 293,270.258 550 2,581.272 0.000 0.963 0.958 0.034 0.064 54.8 18 0.000 0.001 293,289.053 548 2,392.402 0.000 0.967 0.962 0.032 0.035 217.302 24 0.000 0.003 293,104.184 532 2,583.154 0.000 0.963 0.956 0.035 0.037 408.054 8 0.000 0.007 293,326.935 M6 and M7 are tested against M3 Δχ 2 χ 2 for prior model χ 2 for postmodel, Δdf df for prior model df for postmodel, ΔCFI CFI for postmodel CFI for prior model Results Confirmatory Factor Analysis for the Measurement Model We tested two-, three-, and four-factor models sequentially. The two-factor model of pros and cons did not fit well. The three-factor model, splitting social and coping pros, improved the fit. The CFI drops were larger than the critical value, 0.01, showing that three-factor models fitted better than two-factor models. Splitting cons into health and esthetic factors improved the fit over the three-factor model (Table 1). The CFI decreases were greater than the critical value 0.01, providing evidence in favor of the fourfactor structure over the more restrictive three-factor structure previously described. We put the model fit indices for both the second and third occasion in an appendix, available at our website http://www.pcpoh.bham.ac.uk/ primarycare/research/ttm/index.htm. This appendix also includes other descriptive data and analogous model testing with data from the paper questionnaire used in the same trial. All the standardized factor loadings and correlations between factors in these models were statistically significantly different from zero (Fig. 1). There were largemoderate positive correlations between social pros and coping pros and between esthetic cons and health cons. There were smaller negative correlations between each pro and each con. These patterns were similar across all measurement occasions for the computer questionnaire data. Similar results were found for data on the other occasions (see appendix at website above). Measurement Equivalence/Invariance Analysis ME/I test results for the four-factor structure across measurement occasions are shown in Table 2. In model comparisons, all χ 2 changes to the more restricted model were significant but the CFI drops were less than 0.01. This small change in CFI is evidence of configural, metric, scalar, and uniqueness across measurement occasions. The data also supported equal factor means, factor variances, and covariances across measurement occasions. We also tested structural and measurement part between genders and between smokers and nonsmokers, the results of which are in the Electronic Supplementary Material. These results supported measurement and structural part between male and females on each measurement occasion. Measurement and factor variance covariance between smoker/ex-smoker and never-smoker/tried-smoker on each measurement occasion were supported. Structural testing showed, as expected, that the factor means for social pros and coping pros means Table 3 Cronbach s alpha reliability coefficients Round 1 Round 2 Round 3 Social pros 0.749 0.778 0.832 Coping pros 0.813 0.834 0.848 Health cons 0.723 0.787 0.828 Esthetic cons 0.694 0.742 0.771

162 Int. J. Behav. Med. (2009) 16:158 163 were higher for adolescents that smoked compared to those that did not. Similarly, esthetic and health cons means for adolescents that smoked were lower than for nonsmokers. Reliability Cronbach s alpha coefficients for the four pros and cons factors measured on each occasion ranged from 0.694 to 0.848 (Table 3). stage movement [9], though the place of decisional balance in the TTM in smoking acquisition is undefined. These results provide a secure platform for testing the role of decisional balance as cause of stage movement. Evidence in this study of differences in means between smokers and nonsmokers support the hypothesis that changing decisional balance will lead to shifts in stage and behavior. Acknowledgments This study was supported by Cancer Research UK grant C9278/A5639. Paul Aveyard is supported by a National Institute for Health Research Career Scientist Award. Discussion We demonstrated that the simple two-factor pros and cons structure found in adult smoking did not fit adolescent data well. Previous results in adolescents suggested a two- or three-factor structure. Our results suggest a four-factor structure. Also, uniquely, we applied ME/I techniques to examine factorial of the decisional balance structure, showing that the questionnaire performs similarly for smokers and nonsmokers, for girls and boys, and when administered repeatedly, as is the case during interventions. A two-factor structure for decisional balance scale was found for adult smoking and for other health behaviors [21]. In adolescents, Ward et al. [22] found a two-factor structure, although they used a short form of the inventory. Pallonen et al. [5] and Plummer et al. [6], using the same questionnaire as in our study, found a three-factor structure. In neither case were the four-factor alternatives examined. Our results suggest that cons should be subdivided; a finding repeated across all three occasions data were examined. If measurement cannot be established, the finding of between-group differences cannot be unambiguously interpreted [19]. The difference might be true trait differences or respondent s different psychometric responses to the scale items. This study shows ME/I between groups and across measurement occasions. Based on this, we found that smokers and ex-smokers have higher social pros and coping pros means and lower health cons and esthetic cons factor means, compared to those who had never smoked or only experimented. In a companion paper, we showed using multitrait multimethod hierarchical confirmatory factor analysis that there was strong evidence of both convergent and discriminant validity for the decisional balance scales tested [23]. Taken with the results of this investigation and those of Pallonen et al. [5] in smokers only and Plummer et al. 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